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Crash of '87 -- Was it expected?: Aggregate market fears and long-range dependence

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  • Gençay, Ramazan
  • Gradojevic, Nikola

Abstract

We develop a dynamic framework to identify aggregate market fears ahead of a major market crash through the skewness premium of European options. Our methodology is based on measuring the distribution of a skewness premium through a q-Gaussian density and a maximum entropy principle. Our findings indicate that the October 19th, 1987 crash was predictable from the study of the skewness premium of deepest out-of-the-money options about two months prior to the crash.

Suggested Citation

  • Gençay, Ramazan & Gradojevic, Nikola, 2010. "Crash of '87 -- Was it expected?: Aggregate market fears and long-range dependence," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 270-282, March.
  • Handle: RePEc:eee:empfin:v:17:y:2010:i:2:p:270-282
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    1. Rappoport, Peter & White, Eugene N, 1994. "Was the Crash of 1929 Expected?," American Economic Review, American Economic Association, vol. 84(1), pages 271-281, March.
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    Citations

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    Cited by:

    1. Loretta Mastroeni & Pierluigi Vellucci, 2016. "“Butterfly Effect" vs Chaos in Energy Futures Markets," Departmental Working Papers of Economics - University 'Roma Tre' 0209, Department of Economics - University Roma Tre.
    2. Lutz, Chandler, 2015. "The impact of conventional and unconventional monetary policy on investor sentiment," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 89-105.
    3. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    4. repec:eee:ecofin:v:42:y:2017:i:c:p:107-131 is not listed on IDEAS
    5. Nikola Gradojevic & Marko Caric, 2017. "Predicting Systemic Risk with Entropic Indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 16-25, January.
    6. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Chaos" in energy and commodity markets: a controversial matter," Papers 1611.07432, arXiv.org, revised Mar 2017.
    7. Namaki, A. & Koohi Lai, Z. & Jafari, G.R. & Raei, R. & Tehrani, R., 2013. "Comparing emerging and mature markets during times of crises: A non-extensive statistical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3039-3044.
    8. Loretta Mastroeni & Pierluigi Vellucci, 2016. ""Butterfly Effect" vs Chaos in Energy Futures Markets," Papers 1610.05697, arXiv.org.
    9. Alvarez-Ramirez, J. & Rodriguez, E. & Espinosa-Paredes, G., 2012. "A partisan effect in the efficiency of the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4923-4932.
    10. Bekiros, Stelios D., 2015. "Heuristic learning in intraday trading under uncertainty," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 34-49.

    More about this item

    Keywords

    Non-additive entropy Shannon entropy Tsallis entropy q-Gaussian distribution Skewness premium;

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

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